A Comparative Analysis of Text Mining Methodologies for Online Consumer Reviews
Bibliographic record
Abstract
Extracting meaningful insights from the sheer volume of Online Consumer Reviews (OCRs) has been challenging. We aim to explore the most effective methodologies for text mining of OCRs, covering topic extraction, topic classification, and sentiment analysis. Through a comprehensive review of recent research on text mining applied to OCRs, we found that LDA2Vec can enhance the effectiveness of conventional LDA for topic extraction. Additionally, the combination of Convolutional Neural Networks (CNN) and GloVe demonstrates the best performance for topic classification, while CNN and SVM outperform other algorithms for sentiment analysis. Furthermore, the spaCy Natural Language Processing (NLP) proves to be a more effective choice for text pre-processing compared to Natural Language Toolkit (NLTK). Subsequently, we applied these refined models to a Yelp reviews dataset, assessed their performance against conventional models, and provided a comprehensive discussion of the results and limitations. The insights gained from this study can be valuable for developing effective models in OCR analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".